Video based matching and tracking

ABSTRACT

An analytical device is disclosed that analyzes whether a first image is similar to (or the same as) as a second image. The analytical device analyzes the first image by combining at least a part (or all) of the first image with at least a part (or all) of the second image, and by analyzing at least a part (or all) of the combined image. Part or all of the combination may be analyzed with respect to the abstraction of the first image and/or the abstraction of the second image. The abstraction may be based on a Bag of Features (BoF) description, based on a histogram of intensity values, or based on other types of abstraction methodologies. The analysis may involve comparing one or more aspects of the combination (such as the entropy or randomness of the combination) with the one or more aspects of the abstracted first image and/or abstracted second image. Based on the comparison, the analytical device may determine whether the first image is similar to or the same as the second image. The analytical device may work with a variety of images in a variety of applications including a video tracking system, a biometric analytic system, or a database image analytical system.

REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. application Ser.No. 13/049,527 (now U.S. Pat. No. 8,600,172), the entirety of which isincorporated by reference.

BACKGROUND

Video tracking is the process of locating a moving object (or multipleobjects) over time using a camera (or other image capturing device).Video tracking has a variety of uses, some of which include: securityand surveillance; human-computer interaction; video communication andcompression; augmented reality; traffic control; medical imaging; andvideo editing. Video tracking can be a time-consuming process due to theamount of data that is contained in video. Adding further to thecomplexity is the possible need to use object recognition techniques fortracking.

Typically, the objective of video tracking is to associate targetobjects in consecutive video frames. The association can be difficultwhen the objects are moving fast relative to the frame rate. Anothersituation that increases complexity of the problem is when the trackedobject changes orientation over time.

Matching is an important component of the video tracking process inwhich part (or all) of a query image is matched to a part (or all) ofanother image. One general example is to match one image patch with asecond image patch. A more specific example is to match one or moreobjects with one or more image patches in a query image. The object maybe a person (or a part of a person, such as a face), a thing (such as aninanimate object), or the like.

One example of video tracking is illustrated in FIG. 1, which shows asimple example of image tracking a single object. In the present exampledepicted in FIG. 1, object A has been reliably tracked over three frames(shown to the left in FIG. 1). In the fourth frame, which is referred toas the query frame, the goal is to locate the image patch (shown inshading in FIG. 1) that corresponds to the object. In a more generalscenario, there may be multiple objects that are matched and located inthe query frame, and their corresponding image patches can overlapsignificantly. To add to this complexity, there can be lighting andappearance variation of an object from frame to frame. All this makesthe task of matching a challenging problem. Therefore, a need exists toefficiently and accurately perform video tracking.

SUMMARY

An analytical device is disclosed that analyzes whether a first image issimilar to (or the same as) as a second image. In a first aspect, theanalytical device analyzes the first image by combining at least a part(or all) of the first image with at least a part (or all) of the secondimage, and by analyzing at least a part (or all) of the combined image.For instance, an abstraction of part or all of the first image may becombined with an abstraction of part or all of the second image. Part orall of the combination may be analyzed with respect to the abstractionof the first image and/or the abstraction of the second image. Theabstraction may be based on a Bag of Features (BoF) description, basedon a histogram of intensity values, or based on one or more other typesof abstraction methodologies. The analysis may involve comparing one ormore aspects of the combination (such as the entropy or randomness ofthe combination) with the one or more aspects of the abstracted firstimage and/or abstracted second image. Based on the comparison, theanalytical device may determine whether the first image is similar to orthe same as the second image. So that, if the entropy of the combinedimage increases a certain amount (such as increases minimally less thana predetermined level), the analytical device may conclude that thefirst image and the second image are similar. The analytical device maywork with a variety of images in a variety of applications including avideo tracking system, a biometric analytic system, or a database imageanalytical system.

In a second aspect, the analytical device may be a video tracking systemthat utilizes a Bag of Features (BoF) description in its analysis. Thevideo tracking system attempts to track an object by determining whetheran object is present in a query image. The query image may be brokendown into one or more image patches, which may be different parts of thequery image. The analytical device may analyze the one or more of theimage patches in the query image to determine whether the object ispresent in the query image. The analysis may include determining whetherone of the image patches in the query image is similar to the objectbeing tracked. The BoF description of the image patch (and similarly theobject) may represent a cloud of points in multidimensional featurespace. The BoF description may be used in combination with the firstaspect, described above.

In a third aspect, the object subject to tracking may be updated basedon image patches matched from previous frames. The object may initiallybe defined in a variety of ways, such as using the characteristics ofthe image patch to which the object is first matched. In this way, theobject is born or created. The tracking system then attempts to trackthe object in subsequent frames, using the tracking to update thedefinition of the object.

Other systems, methods, features and advantages will be, or will become,apparent to one with skill in the art upon examination of the followingfigures and detailed description. It is intended that all suchadditional systems, methods, features and advantages be included withinthis description, be within the scope of the invention, and be protectedby the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of the matching problem in an objecttracking scenario.

FIG. 2 is a block diagram of a system for analyzing one or more images.

FIG. 3 is a block diagram of a video tracking system.

FIG. 4 is a block diagram of a biometric analytical system.

FIG. 5 is a block diagram of database image analytical system.

FIG. 6 is an illustration of an abstraction of an image patch withpoints within the image patch described in terms of fundamentalfeatures.

FIG. 7 is one example of the construction of a Bag of Features (BoF)matrix B of an image patch with only five points in it, wherein each rowin matrix B corresponds to point in the patch, and with the first,second and the third column of matrix B corresponds to the x-location,y-location and the grayscale value of the point(s).

FIG. 8 is an illustration of the BoF matrix B for the image patch beingrepresented as a cloud of points in high dimensional feature space.

FIG. 9 is an illustration of two observation image patches, which arecompared by first computing their BoF, followed by merging theirprobability density functions.

FIG. 10 is an illustration showing a sequence of the matching process,in which several subregions (shown in highlights) of an image patch arequeried as possible matches to the object, with the BoF description foreach subregion being computed separately, and the subregion that isclosest to the object being chosen as the match.

FIG. 11 is an example of a flow diagram for matching an image patch toan object.

FIG. 12 is a table illustrating the change in asset scores correspondingto the reference datasets.

DETAILED DESCRIPTION

An analytical device is disclosed that analyzes whether a first image issimilar to (or the same as) as a second image. In a first embodiment,the analytical device analyzes the first image by combining at least apart (or all) of the first image with at least a part (or all) of thesecond image, and by analyzing at least a part (or all) of the combinedimage. For instance, an abstraction of part or all of the first imagemay be combined with an abstraction of part or all of the second image.Part or all of the combination may be analyzed with respect to theabstraction of the first image and/or the abstraction of the secondimage. The abstraction may be based on a Bag of Features (BoF)description, based on a histogram of intensity values, or based on oneor more other types of abstraction methodologies. The analysis mayinvolve comparing one or more aspects of the combination (such as theentropy or randomness of the combination) with the one or more aspectsof the abstracted first image and/or abstracted second image. Forexample, the analysis may comprise calculating a randomness parameterusing the entropy measure based on information theory. Based on thecomparison, the analytical device may determine whether the first imageis similar to or the same as the second image. So that, if the entropyof the combined image increases minimally, the analytical device mayconclude that the first image and the second image are similar.

The analytical device may work with a variety of images in a variety ofapplications. For example, the analytical device may comprise a videotracking system in which a part (or all) of the query frame may becompared with an object. The object may be defined based on matches toimages in previous frames (such as an image patch in a previous frame ora combination of image patches in multiple previous frames). As anotherexample, the analytical device may be used in biometrics. For example,in face biometrics, a query face image may be compared with one or morestored images, with the analytical device scoring and/or ranking thecomparison of query face image with the one or more stored images. Asstill another example, the analytical device may be used to analyzeimages in large databases. In particular, the analytical device mayanalyze a query image in the database to determine whether the queryimage is similar with one or more stored images in the database (such asone or more separate images or one or more frames in a video).Similarity may be indicated based on a score and/or rank of thecloseness of the query image with one, some, or all the stored images.

In a second embodiment, the analytical device may be a video trackingsystem that utilizes a Bag of Features (BoF) description in itsanalysis. The video tracking system attempts to track an object bydetermining whether an object is present in a query image. As discussedabove, the object may be a person (or a part of a person), an object, orthe like. The query image may be broken down into one or more imagepatches, which may be different parts of the query image. The analyticaldevice may analyze the one or more of the image patches in the queryimage to determine whether the object is present in the query image. Theanalysis involves determining whether one of the image patches in thequery image is similar to the object being tracked. The BoF descriptionof the object and the image patches enable the capture of rich semanticinformation of the object and the image patch, and hence aid in betterdetermining whether the object is similar to one of the image patches.In one aspect, the BoF description of the image patch (and similarly theobject) represents a cloud of points in multidimensional feature space.The BoF description may be used in combination with the firstembodiment, described above.

In a third embodiment, the object subject to tracking is updated basedon image patches matched from previous frames. As discussed above, theobject may be the person or thing being tracked in the video. The objectmay initially be defined in a variety of ways. One way is to assign thecharacteristics of the image patch to which the object is first matched.For example, after foreground/background segmentation, one or more imagepatches (or “blobs”) may be found in the current video frame (e.g.,frame 1). If there are no objects in the current list to track, or ifone of the image patches does not match to any of the objects on thecurrent list to track, the image patch without a matched object may beused to characterize the object (e.g., image_patch_(frame 1)). In thisway, the object is born or created. The tracking system then attempts totrack the object in subsequent frames.

In the subsequent frame, after foreground/background segmentation, oneor more image patches are found in the current video frame (e.g., frame2). Further, using image_patch_(frame 1), the object is matched to oneof the image patches in frame 2 (e.g., image_patch_(frame 2)). After amatch of the object to one of the image patches in frame 2, thedefinition of the object is updated based on the match. For example,image_patch_(frame 1) and image_patch_(frame 2) may be used to define orcharacterize the object. In this way, the definition of the object maychange dynamically from one frame to the next. In the instance where theimage patch is defined as a cloud of points in a feature space,combining image_patch_(frame 1) and image_patch_(frame 2) may beaccomplished by fusing the two clouds.

Referring to FIG. 2, there is illustrated an analytical device 210,which may comprise an electronic device such as a computer. Theanalytical device 210 may include a controller 212, such as a processor,microcontroller, or other type of arithmetic logic unit. The analyticaldevice may further include a storage element 214, which may comprisevolatile and/or non-volatile memory, and may include one or moreprograms stored thereon. The storage element 214 may comprises aninternal or externally accessible database, and may store one or moreimages.

The analytical device 210 may communicate, via a communication interface216, with an input/output device 220. The input/output device 220 may bean input device, an output device, a combination of an input device andan output device, or a separate input device and a separate outputdevice. As discussed subsequently, the analytical device 210 may work incombination with a video device (such as a video recorder or otherdevice that records images and/or audio), as shown in FIG. 3, abiometric input device/control device, as shown in FIG. 4, or animage/video database, as shown in FIG. 5.

The storage element 214 may be a main memory, a static memory, or adynamic memory. The storage element 214 may include, but may not belimited to, computer-readable storage media such as various types ofvolatile and non-volatile storage media including, but not limited to,random access memory, read-only memory, programmable read-only memory,electrically programmable read-only memory, electrically erasableread-only memory, flash memory, magnetic tape or disk, optical media andthe like.

In one case, the storage element 214 may include a cache or randomaccess memory for the controller 212. Alternatively or in addition, thestorage element 214 may be separate from the controller 212, such as acache memory of a processor, the system memory, or other memory. Thestorage element 214 may be an external storage device or database forstoring data. Examples may include a hard drive, compact disc (“CD”),digital video disc (“DVD”), memory card, memory stick, floppy disc,universal serial bus (“USB”) memory device, or any other deviceoperative to store data. The storage element 214 may be operable tostore instructions executable by the controller 212. The functions, actsor tasks illustrated in the figures (such as FIG. 11) or describedherein may be performed by the programmed controller 212 executing theinstructions stored in the storage element 214. The functions, acts ortasks may be independent of the particular type of instruction set,storage media, processor or processing strategy and may be performed bysoftware, hardware, integrated circuits, firm-ware, micro-code and thelike, operating alone or in combination. Likewise, processing strategiesmay include multiprocessing, multitasking, parallel processing and thelike.

The analytical device 210 may also include a communication interface 216to enable communication with multiple devices via the network. Thecommunication interface 216 may be created in software or may be aphysical connection in hardware. The communication interface may beconfigured to connect with a network, external media, the display, orany other components in system, or combinations thereof. The connectionwith the network may be a physical connection, such as a wired Ethernetconnection or may be established wirelessly as discussed below.Likewise, the additional connections with other components of the systemmay be physical connections or may be established wirelessly.

The analytical device 210 may optionally include a display, such as aliquid crystal display (LCD), an organic light emitting diode (OLED), aflat panel display, a solid state display, a cathode ray tube (CRT), aprojector, a printer or other now known or later-developed displaydevice for outputting determined information. The display may act as aninterface for the user to see the functioning of the controller 212, orspecifically as an interface with the software stored in the storageelement 214 or in the drive unit.

Additionally, the analytical device 210 may optionally include an inputdevice configured to allow a user to interact with any of the componentsof system. The input device may be a number pad, a keyboard, or a cursorcontrol device, such as a mouse, or a joystick, touch screen display,remote control or any other device operative to interact with thesystem.

The analytical device 210 may also optionally include a disk or opticaldrive unit. The disk drive unit may include a computer-readable mediumin which one or more sets of instructions, e.g. software, can beembedded. Further, the instructions may perform one or more of themethods or logic as described herein. The instructions may residecompletely, or at least partially, within the storage element 214 and/orwithin the controller 212 during execution by the computer system. Thestorage element 214 and the controller 212 also may includecomputer-readable media as discussed above. For example, theinstructions to perform the actions illustrated in FIG. 11 (describedbelow) may be included in the storage element 214.

The present disclosure contemplates a computer-readable medium thatincludes instructions or receives and executes instructions responsiveto a propagated signal. The instructions may be implemented withhardware, software and/or firmware, or any combination thereof. Further,the instructions may be transmitted or received over the network via acommunication interface 216.

Referring to FIG. 3, there is illustrated a tracking device 310, whichmay track one or more objects in a video or in a series of still imagesrepresenting scenes of motion. The tracking device 310 includes acontroller 212, which may be the same as controller 212 in theanalytical device 210. The tracking device 310 further includes astorage element 314, which may include volatile or non-volatile memory.The storage element 314 may store archival or historical videorecordings or archival or historical still images, and may storereal-time video recordings and still images. The tracking device 310 mayalso include a communication interface 316 in order for the trackingdevice 310 to communicate with a video device 320. The video device 320may be a video camera, or a still camera which is configured to obtain aseries of still images.

Referring to FIG. 4, there is shown a biometric analytical device 410,which may analyze one or more biometric inputs. The biometric analyticaldevice 410 may include methods by which to uniquely recognize humansbased upon one or more intrinsic physical or behavioral traits. Forexample, biometrics may be used as a form of identity access managementand access control. Biometrics may also be used to identify individualsin groups that are under surveillance.

Biometrics may relate to physiological unique traits. Examples of suchtraits include, but are not limited to fingerprint, face recognition,DNA, Palm print, hand geometry, iris recognition, retinal scan, andodor/scent. Biometrics may also relate to the behavior of a person.Examples include, but are not limited to typing rhythm (e.g., keystrokedynamics), gait, and voice.

The biometric analytical device 410 includes a controller 212, which maybe the same as controller 212 in the analytical device 210. Thebiometric analytical device 410 further includes a storage element 414,which may include volatile or non-volatile memory. The storage element414 may store the captured biometric (such as a copy of a particularperson's or an identifiable fingerprint, face recognition, DNA, palmprint, hand geometry, iris recognition, retinal scan, etc.). The storageelement 414 may further store biometric inputs (such as an image)received from a biometric input device 420 in order to compare with thecaptured biometric. The biometric analytical device 410 may also includea communication interface 416 in order for the biometric analyticaldevice 410 to communicate with the biometric input device 420 and acontrol device 430. The biometric input device 420 may include afingerprint scanner, a retinal scanner, an iris scanner, or the like.The control device 430 may control access to one or more areas andresources in a given physical facility or control access to acomputer-based information system. For example, the control device 430may control access to an area (such as an electronic lock), or maycontrol access to an electronic device (such as a computer, a database,etc.).

Referring to FIG. 5, there is shown an image analytical device 510,which may analyze one or more images. The image analytical device 510may include methods by which to analyze multiple images stored on imagedatabase 520. The image analytical device 510 includes a controller 212,which may be the same as controller 212 in the analytical device 210.The image analytical device 510 further includes a storage element 414,which may include volatile or non-volatile memory, and may store stillimages and/or video. A sample image may be compared against one or moreimages stored in a database. The result of the comparison may be adetermination of which of the images stored in the database are similarto the sample image. For example, the analysis may be used in order toreduce the number of duplicate images that are stored in a database. Asanother example, the analysis may be used to determine whether thesample image was present in an archival database. In particular, if aparticular image (such as of a person) is designated as the sampleimage, the analysis may review the images in archival video footage todetermine whether the person was present in any of the images of thedatabase.

As discussed above, the analytical device 210 may compare two differentabstractions, and determine whether the abstractions are similar to oneanother. As one example, the analytical device may compare anabstraction of a first image (or a part of the first image) with anabstraction of a second image (or a part of the second image). Theabstractions may then be used to track an object, to analyze a biometricinput, or to analyze an image in a database. As discussed in more detailbelow, there are several ways in which to abstract an image patch or anobject, including without limitation, a BoF description and a histogramof intensity values. The abstraction of the first image (or a part ofthe first image) may be at least partly combined with the abstraction ofthe second image (or a part of the second image), and the combinationmay be analyzed to determine whether the first image (or the part of thefirst image) is similar to the second image (or the part of the secondimage).

Similarly, the abstraction of the object being tracked (or a part of theobject being tracked) may be combined with the abstraction of a queryimage (or a part of the query image), and the combination may beanalyzed to determine whether the object being tracked (or the part ofthe object being tracked) is similar to the query image (or the part ofthe query image). In particular, the analytical device may compare anobject being tracked (such as a person's face or body) with an imagepatch in a query frame in order to determine whether the object issimilar to the image patch in the query frame. The object may berepresented by an abstract representation, as discussed in more detailbelow, so that the abstract representation of the object may be combinedwith the abstract representation of the image patch. The combination maybe analyzed to determine whether the object (as defined by its abstractrepresentation) is similar to the image patch (as defined by itsabstract representation).

One type of abstraction is the BoF based description. BoF focuses ondefining an object or an image (or a part of an image, such as an imagepatch) based on various features. FIG. 6 illustrates an example of anapplication of the BoF based description. FIG. 6 illustrates an imagepatch (show as the shaded blob), with points within the image patchrepresented as circles. The BoF description may focus on features atvarious parts of the image patch (such as at the points within the imagepatch as shown in FIG. 6). The BoF based description may be representedmathematically in a variety of ways, such as by using a BoF matrix, B,which characterizes the image patch. Referring to the example depictedin FIG. 6, the image patch may constitute “N” points, where each pointcan be described in terms of fundamental features, such as its Xlocation, its Y location, its color value, its grayscale value, etc. Ingeneral, if each of the N points is described in terms of M features,this would lead to a BoF matrix B, where

$\begin{matrix}{B = {\begin{bmatrix}f_{1}^{1} & f_{1}^{2} & \ldots & f_{1}^{M} \\f_{2}^{1} & f_{2}^{2} & \ldots & f_{2}^{M} \\\vdots & \vdots & \ldots & \vdots \\f_{N}^{1} & f_{N}^{2} & \ldots & f_{N}^{M}\end{bmatrix}.}} & (1)\end{matrix}$

In the example given as shown in Equation (1), the BoF matrix includes[x, y, YImage, cbImage, crImage, GradientInYImage, OrientationInYImage]as the 7 features that describe a point in the image patch. For example,“x” and “y” represent the “X” and “Y” location of the point within theimage patch. The YImage, cbImage, and crImage, represent Y Cb Cr, whichis one way of encoding RGB information. These features are merely forillustration purposes. Other features may be used in addition to, orinstead of, the features listed.

The ith row in this matrix corresponds to the ith point in the imagepatch. The jth column in the matrix corresponds to the jth feature. FIG.7 illustrates a simpler example of the construction of a BoF matrix B ofa patch with only five points in it. Each row in matrix B corresponds topoint in the patch. The first, second and the third column of Bcorresponds to the x-location, y-location and the grayscale value of thepoint(s).

The BoF matrix can be visualized in the high dimensional feature spaceas well. For the simple example illustrated in FIG. 7, in a3-dimensional space spanned by the x-value, y-value, and grayscalevalue, there is a “cloud” of 5 points. In general, the mapping may beillustrated from an image patch/region to a cloud of points in thefeature space, such as depicted in FIG. 8 in which the BoF matrix B fora patch is represented as a cloud of points in the high dimensionalfeature space. The statistics of the cloud in this high dimensionalspace may capture the characteristics of the image region. Moreover, thecloud may be described in a variety of ways. One way is by using aprobability density function (PDF) of the cloud as the descriptor of thecloud.

Another type of abstraction is the histogram of intensity values. Forexample, an image patch B1 may be described simply using histograms ofintensity values. One may assume: N data points with scalar values f₁, .. . f_(N), each f_(i) are real numbers; and m intervals/bins defined bypoints b₀, b₁, . . . b_(m), where b_(i)<b_(i+1) (assuming uniformlyspaced bins, then b_(i+1)−b_(i)=b for i=1, . . . , m-1). The histogramh=(h₁, . . . , h_(m)) records the number pints f_(j) that fall into eachbin. It may be calculated as follows: Set h=(0, . . . , 0), then, fori=1: N, find the j such that b_(j)≦f_(i)<b_(j)+1 and set h_(j)=h_(j)+1.

There are several variants of the histogram of intensity values that maybe used to describe image patches, including without limitation:co-occurrence histograms; histogram of gradients; auto-correlograms.

After abstraction of the different image regions, the abstracted imageregions may be compared. In one embodiment, the abstracted image regionsare at least partly combined (such as combining some or all of theabstracted image regions), and at least a part (or all) of the combinedabstracted image regions is analyzed. In a more specific embodiment, thecombined abstracted image region is compared with one or both of theunderlying abstracted image regions.

As discussed above, one way to abstract an image region is by computinga BoF matrix (and the corresponding PDF cloud) for the entire imageregion. To that end, in order to compare two image regions, theassociated BoF matrix (and the PDF cloud) for each of the image regionsare computed. To compare the two BoF matrices and hence their associatedPDF cloud, the two PDFs are merged to form one single PDF. The singlePDF is then analyzed. In one embodiment, the single PDF is analyzed forat least one aspect, such as randomness. For example, the single PDF(which was merged from the two PDFs) is compared with one (or both) ofthe two PDFs to determine randomness of the single PDF (such asdetermining whether the single PDF is more random than one (or both) ofthe two PDFs or determining how much more random the single PDF iscompared to the two individual PDFs).

Alternatively, the image region may be divided into multiple sub-regions(such as 4 sub-regions), and the BoF representation may be generated foreach of the multiple sub-regions. When comparing two image regions, theanalytical device may compare each of the multiple sub-regionsindividually, or the entire image regions's BoF representation, andselect the “best” score according to one or more specific rules.Examples of specific rules include without limitation a minimum rule (inwhich the image region and/or particular image sub-region is selectedthat has associated with it the minimum score), or a maximum rule (inwhich the image region and/or particular image sub-region is selectedthat has associated with it the maximum score).

Another way to abstract an image region is with an intensity histogram.In order to compare two patches B₁ and B₂, a normalized histogram H₁ isgenerated for B₁ and a normalized histogram H₂ is generated for B₂. Thehistograms (H₁ and H₂) may be combined, such as by creating an averagehistogram H_(avg)=0.5(H₁+H₂). The combined histogram may be analyzed inorder to determine the similarity or dissimilarity between patches B1and B2 as follows:

Delta(B 1, B 2) = Entrophy(H) − 0.5[Entropy(H 1) + Entropy(H 2)]${{where}\mspace{14mu} {{Entrophy}(H)}} = {- {\sum\limits_{i = 1}^{n}\; {h_{i}{{\log_{e}( h_{i} )}.}}}}$

in which [h₁, h₂, . . . , h_(n)] are the bin values of the normalizedhistogram H.

B1 could be described simply using histograms of intensity values. Thedissimilarity measure in Equation 2 (described below) may then bedefined on the histograms in a similar manner. As an example, H1 may bethe normalized histogram of B1 and H2 may be the normalized histogram ofB2. An average histogram H=0.5(H1+H2) thus results.

Referring to FIG. 9, there is shown one graphical example of generatingthe merged abstractions of the image regions. More specifically, FIG. 9illustrates two observation image patches (Obs1 and Obs2 in FIG. 9) thatare compared by first computing their BoF description, which isrepresented by their PDF clouds (pdf1 and pdf2 in FIG. 9). Theabstractions of the observation image patches may be combined by mergingof their PDF clouds (as shown in the combined pdf in FIG. 9). Asdiscussed above, at least one aspect (such as randomness) of the mergedcloud may be analyzed. One example of the analysis may include usingentropy functions, in which H( ) corresponds to the entropy functioncomputed for a given PDF. The dissimilarity metric that compares the twoimage clouds is:

delta(obs1, obs2)=H(combined_pdf)—0.5[H(pdf1)+H(pdf2)]

Here obs1 has a probability distribution function of pdf1, and the meanand covariance corresponding to the distribution is (μ_(G1), Σ_(G1)).Similarly, obs2 has a probability distribution function of pdf2, and themean and covariance corresponding to the distribution is (μ_(G2),Σ_(G2)). The combined distribution's PDF's mean and covariance matricesare (μ_(G), Σ_(G)).

Using the assumption that the pdfs' follow a Gaussian distribution,H(pdf1)=K+0.5 log(determinant(Σ_(G1))), H(pdf2)=K+0.5log(determinant(Σ_(G2))), and H(combined_pdf)=K+0.5log(determinant(Σ_(G))), where K is a constant.

Thus, delta(obs1, obs2)=K+0.5 log(determinant(Σ_(G)))−0.5[2K+0.5log(determinant(Σ_(G1))+0.5 log(determinant(Σ_(G2)))]. Hence,

$\begin{matrix}{{{delta}( {{{obs}\; 1},{{obs}\; 2}} )} = {\log {\frac{\Sigma_{G}}{\sqrt{{\Sigma_{G\; 1}}{\Sigma_{G\; 2}}}}.}}} & (2)\end{matrix}$

In the above formula, Σ_(G) corresponds to the covariance matrixcorresponding to the combined PDF, while Σ_(G1) and Σ_(G2) correspond tothe covariance matrices associated with PDFs for obs1 and obs2,respectively.

Equation 2 provides an example of how two multivariate Gaussiandistributions may be compared. A cloud of points in a feature space(such as a PDF cloud illustrated in FIG. 8) may be described by amultivariate Gaussian with mean μ1 and variance S1. If the first cloudis compared to a second cloud with mean μ2 and variance S2, one mayfirst “fuse” the two clouds to create an intermediate description. Ifthe intermediate description is random or chaotic, one may conclude thatthe two clouds of points that are being compared are quite dissimilar.On the other hand, if the intermediate description of the cloud is lesschaotic, it would imply that the two initial clouds being compared arevery similar.

The following illustrates how one may compute Σ_(G) from Σ_(G1) andΣ_(G2), respectively. Since it may not be computationally practical tocarry the BoF matrix and the corresponding cloud in the high dimensionalspace, especially for tracked objects, the BoF matrix and thecorresponding PDF may be represented using the M×1 mean vector and theM×M covariance matrix Σ instead. The (i,j)th element of the matrix Σ_(G)can be expressed in terms of the elements in μ_(G1), Σ_(G1), μ_(G2),Σ_(G2) as

Σ_(G)(i,j)=0.5[Σ_(G1)(i,j)+Σ_(G)(i,j)+μ_(G1)(i)μ_(G1)(j)+μ_(G2)(i)μ_(G2)(j)]−μ_(G)(i)μ_(G)(j),

where

μ_(G)(i)=0.5[μ_(G1)(i)+μ_(G2)(i)] and μ_(G)(j)=0.5[μ_(G1)(j)+μ_(G2)(j)],respectively   (3)

Thus, Equation 3 illustrates how to combine the mean vectorcorresponding to the first cloud with the mean vector corresponding tothe second cloud in order to compute the mean vector of the intermediateor the fused cloud. It is essentially the average of the two meanvectors.

Choosing to ignore the μ-component of the statistics, the formula tocreate the merged covariance matrix simplifies further to:

Σ_(G)(i,j)=0.5[Σ_(G1)(i,j)+Σ_(G2)(i,j)].   (4)

Thus, Equation 4 states that the covariance matrix of the intermediateor the fused cloud is an average of the individual covariance matrices.Of note, an object BoF is essentially a collection of the BoF of theindividual image patches (one per frame) that have been matched to theobject. Hence, the mean vector and the covariance vector of the objectis computed and updated using Equation 3 or Equation 4, where G1corresponds to the Gaussian describing the object, and G2 corresponds tothe image patch that matched the object. After the match is established,G corresponds to the Gaussian describing the object.

One computation issue is the matching process of the image patch withthe object. During the matching process, several subregions of the imagepatch may be queried. This is depicted in FIG. 10, in which the variousboxes represent the subregions. In particular, during the matchingprocess, several subregions (shown in boxes) of an image patch arequeried as a possible match to the object. The BoF description for eachpatch may be computed separately, and the patch that is closest to theobject is chosen.

Moreover, computing the mean vector and the variance matrix for a patchwith N points involves several multiplications and additions. Forexample, the computation of Σ_(G1)(i,j) for obs_1 involves O(N)multiplications and O(N) additions. During the matching process intracking, the computation of this element in the covariance matrix isrepeated for several query patches in the image. Of note is that theremay be several redundant elements that are computed during the searchprocess. To streamline the process, the covariance matrix elements arecomputed using the integral image approach. For M underlying features,M(M-1)/2 cross images are computed. For example, the x-y cross imagecorresponds to the multiplication of the x-Image and the y-Image of thepoints in the patch, where x-Image corresponds to the image where the(k, l)th location in the image corresponds to the x-value of this pointin the image, and so on. The integral image of each of the cross imagesis also computed. The (k, l)th location of the integral image stores thesum of all the entries corresponding to the sub image with (0,0) and (k,l) being the top-left and the bottom-right points of the image. Althoughthe computation of the M(M-1)/2 cross image and the integral image mayappear to be a computationally expensive task, the SSE instruction setsmay speed up the computation. Since integral image computation is arecursive operation, where the value of the (k, l)th location depends onthe neighboring values of the image, it may not be able to compute themin parallel using a naive implementation. In order to perform thecomputation, interleaving of the M(M-1)/2 integral images is performed,and observing that the (k, l)th location value of two integral imagescan be computed independent of each other. This assists in parallelizingthe summation operations of the integral image computation through anSSE implementation.

The comparison of the two abstractions may generate various results,such as a score or a rank. For example, the abstractions for an objectand an image patch may be compared, and a score or rank may be generatedbased on the comparison. As discussed in more detail in FIG. 11, a scoreor a rank above or at a predetermined amount may result in adetermination of a “match” between the object and the image patch.Alternatively, when there are multiple image patches, the particularimage patch/object combination that results in the highest score forsimilarity (or the lowest score if dissimilarity is the indicator) isdeclared the “match”.

Further, as discussed in FIG. 11, after an image patch is matched to anobject, the definition of the object may be updated based on the matchedimage patch. One manner of updating the definition of the object is byfusing the abstraction of the image patch which is matched to the objectwith the abstraction of the object. Another manner of updating thedefinition of the object is by using the abstraction of the image patchas one mode in a multimodal abstraction. In particular, even when theobject and the image patch are declared as a match, the dissimilarityvalue may be above a certain threshold (such as 0.2 for example). Inthose instances, instead of fusing the abstraction of the image patch tothe abstraction of the object to maintain one single statisticaldescription, a “multimodal” distribution may be used, such as amultimodal Gaussian distribution for the underlying BoF model of theobject. In this way, an abstraction of an object may have severalunderlying Gaussian distributions, with each such distribution having anunderlying mean and covariance matrix associated with it. When comparingan object with an image patch, the image patch Gaussian (whichrepresents the image patch abstraction) may be compared to each of theobject patch Gaussian using Equation 2 and the minimum value may beselected as the match dissimilarity. If it is later determined that theobject has indeed matched the image patch, the object's BoF descriptionis updated by either merging the image patch's Gaussian with thematching Gaussian corresponding to the object, or appending it to theobject's BoF Gaussian set.

Therefore, at least a part of the combined image may be analyzed andcompared with at least a part of each (or both) of the underlyingimages. For example, the entropy may be computed for the combined image,and compared with each (or both) of the underlying images, such as bysubtracting the entropy for both of the underlying images (e.g.,subtracting the average of the entropy for both of the underlyingimages). The result of subtracting the average entropy for both of theunderlying images from the entropy of the combined image may then beanalyzed. For example, the result of subtracting the average entropy forboth of the underlying images from the entropy of the combined image maybe compared to a threshold (such as the 0.2 threshold discussed above).If the result is equal to or less than the threshold, it may bedetermined that the underlying images (such as the object and the imagepatch) are a match. Alternatively, if the result is greater than thethreshold, it may be determined that the underlying images (such as theobject and the image patch) are not a match.

Referring to FIG. 11, there is shown a flow diagram 1100 for matching animage patch to an object. At 1102, foreground/background segmentation isperformed in order to generate list of potential image patches incurrent frame. The foreground/background segmentation may generatemultiple image patches for the current frame. The image patches may beof any size and shape, and are not limited to the rectangular shapes asshown in FIG. 1.

At 1104, the first image patch is selected. An abstraction of the firstimage patch may be generated, and at 1106, combined with the abstractionof the object. As discussed above, various types of abstractions for theimage patch may be used, including BoF description and a histogram ofintensity values. Further, the abstraction of the object may be a BoFdescription and a histogram of intensity values, and may be a singlemode or multimodal, as discussed above. For example, the two images(sample image patch and the object) may be abstracted into statisticalrepresentations of individual clouds (C₁ and C₂). The statisticalrepresentations (C₁ and C₂) may then be combined to form a combinedcloud (C).

At 1108, the combination may be analyzed. The analysis of thecombination may be a comparison of the combination with one, some, orall of the abstractions used to generate the combination. For example,the combination (based on the abstraction of the image patch with theabstraction of the object) may be analyzed against the abstraction ofthe image patch and analyzed against the abstraction of the object. Theanalysis may be used to determine at least one feature of thecombination as compared to the underlying image patch and object, suchas the extent by which the randomness of the combination increases whencompared to the image patch and when compared to the object. Asdiscussed above, randomness is an indicator of whether the combinationis more or less similar to the underlying image patch. The analysis ofthe combination may generate a score, which may be used thereafter torank the various combinations.

In one embodiment, the analysis in 1108 may use a single metric (such asthe BoF metric or the histogram metric). For example, the BoF metric maybe used to generate BoF clouds of the image patch and the object. TheBoF clouds of the image patch and the object may be combined to generatea BoF combined cloud. Further, the BoF combined cloud may be comparedwith the BoF cloud of the image patch and the BoF cloud of the object todetermine the entropy or randomness of the combined cloud minus theaverage entropy or randomness of the individual clouds. Similarly, thehistogram metric may be used to abstract the image patch and the object,with the abstractions being combined to generate a combined abstraction.

Assuming that the clouds follow Gaussian statistics, the entropy ofcloud C₁ is the following:

C ₁ =K+0.5 log(determinant(Σ_(G1)))

where K is a constant and is the covariance matrix of the Gaussianrepresenting the statistics of the cloud. In this way, the entropy orrandomness may simplify to Equation 2, discussed above.

Alternatively, the analysis in 1108 may use a combination of metrics(such as the BoF metric and the histogram metric). As merely oneexample, one or more threshold values may be used for the BoF metric,and one or more threshold values may be used for the histogram metric.In particular, t₁ and t₂ may be used for the BoF metric, with t₁ beingless than t₂. Moreover, t₃ may be used for the histogram metric. Examplevalues of t₁, t₂, and t₃ are 0.7, 1.5, and 0.5, respectively. Theexample values are merely for illustration purposes.

In practice, the comparison of the combination with the image patch usedto generate the combination results in an indication of the closeness ofthe combination image with the underlying image patch. For example,using the BoF metric to analyze the combination results in d_b, anindicator of closeness of the combination with the underlying imagepatch. Likewise, using the histogram metric to analyze the combinationresults in d_h, an indicator of closeness of the combination with theunderlying image patch.

One example of application of the thresholds is as follows:

If the d_b<t₁, then the indication of closeness is set equal to d_b.

If the d_b>t₁ and d_h<t₃, then the indication of closeness is set equalto d_h.

If d_h>t₃, then the indication of closeness is set equal to infinity(meaning that the combination and one of the image patches do notcorrelate or match at all.

At 1112, it is determined whether there is another image patch toanalyze. If so, the next image patch is selected at 1110, and the looprepeats. If not, the analytical device determines which of the imagepatches matches the object based on the analyses at 1114. The analyticaldevice may make this determination by examining one, some, or all of thecombinations. For example, the analytical device may score each of thecombinations based on the analysis at 1108, and may thus rank thecombinations based on the assigned scores. The rank may indicate whichof the combinations is most similar to the underlying abstraction of thecorresponding image patch and/or object, and in turn indicate which ofthe image patches is most similar to the object. Thus, the ranking mayindicate which of the image patches matches the object.

At 1116, the description of the object may be updated based on thematched image patch. One example of updating is to replace thedefinition of object O₁ by the definition of the image patch to whichthe object was matched in the current frame. Another example of updatingis to fuse the definition of object O₁ with the definition of the imagepatch to which the object was matched in the current frame. An exampleof fusing is illustrated in FIG. 9. Still another example of updated isto add the definition of the matched image patch to the definition ofthe object O₁. As discussed above, the abstraction of the object O₁ maybe multimodal. Adding the definition of the matched image patch to thedefinition of the O₁ may comprise adding a mode as defined by thematched image patch.

Referring to FIG. 12, there is shown a table illustrating the change inasset scores corresponding to the reference datasets. Using BoF baseddissimilarity computation to drive the matching process, one may observean improvement in the asset and tracking scores on some of the referencedatasets. The BoF description may capture the semantics of an object inan image in detail, and this is reflected in the improved scores. In asimilar manner, tracking score results, both for indoor and outdoorscenes, may improve. From the computational load viewpoint, overallexecution time may increase; however, the increase in computational timeusing the BoF may be less than 5% of the time while using the histogrambased matching approach. In order to further reduce BoF computationload, the BoF features need only be performed on the foreground patch(or blob) areas.

While the invention has been described with reference to variousembodiments, it should be understood that many changes and modificationscan be made without departing from the scope of the invention. It istherefore intended that the foregoing detailed description be regardedas illustrative rather than limiting, and that it be understood that itis the following claims, including all equivalents, that are intended todefine the spirit and scope of this invention.

1-17. (canceled)
 18. A method of analyzing a known image abstraction anda query image abstraction to determine whether a query image is similarto a known image, the method comprising: accessing the known imageabstraction, the known image abstraction being based on part or all ofthe known image; accessing the query image abstraction, the query imageabstraction being based on part or all of the query image, the knownimage and the query image being of a same type; combining at least apart of the known image abstraction with at least a part of the queryimage abstraction to generate a combined image abstraction; comparingthe combined image abstraction with at least a part of one or both ofthe known image abstraction and the query image abstraction; anddetermining whether the query image is similar to the known image basedon comparing the combined image abstraction with at least a part of oneor both of the known image abstraction and the query image abstraction.19. The method of claim 18, wherein comparing the combined imageabstraction with at least a part of one or both of the known imageabstraction and the query image abstraction comprises determiningwhether the combined image abstraction is less chaotic than chaosassociated with each of the known image abstraction and the query imageabstraction.
 20. The method of claim 18, wherein the known imageabstraction comprises a known Bag of Features (BoF) description, theknown BoF description representing a cloud of points in multidimensionalfeature space; and the query image abstraction comprises a query BoFdescription.
 21. The method of claim 18, wherein comparing the combinedimage abstraction with at least a part of one or both of the known imageabstraction and the query image abstraction includes comparing entropyof the combined image with entropy of one or both of the known imageabstraction and the query image abstraction.
 22. The method of claim 18,wherein the known image abstraction comprises an object abstractionassociated with an object being tracked by a video tracking system; andwherein the query image abstraction is associated with an image patch ina video frame.
 23. The method of claim 22, further comprising:performing foreground/background segmentation to generate a list ofmultiple image patches in the video frame; generating an abstraction foreach of the multiple image patches; iteratively combining the objectabstraction with each of the abstractions for the multiple image patchesto generate combined abstractions; and analyzing the combinedabstractions to determine which of the multiple image patches is mostsimilar to the object abstraction.
 24. The method of claim 23, whereinthe object abstraction is updated based on the image patch most similarto the object abstraction.
 25. The method of claim 24, wherein theobject abstraction is fused with the image patch most similar to theobject abstraction.
 26. The method of claim 24, wherein the objectabstraction comprises a multimodal abstraction; and wherein one mode ofthe multimodal abstraction is modified using the image patch mostsimilar to the object abstraction.
 27. The method of claim 24, whereinthe object abstraction comprises an object cloud of points; wherein eachof the multiple image patches comprise a respective cloud of points; andwherein generating the combined image abstractions comprises fusing theobject cloud of points with each of the respective clouds of points. 28.The method of claim 18, wherein comparing the combined image abstractionwith at least a part of one or both of the known image abstraction andthe query image abstraction comprises generating a score indicative ofrandomness of the combined image abstraction with at least a part of oneor both of the known image abstraction and the query image abstraction;and wherein determining whether the query image is similar to the knownimage comprises comparing the score with a predetermined amount.
 29. Themethod of claim 18, wherein comparing the combined image abstractionwith at least a part of one or both of the known image abstraction andthe query image abstraction comprises: determining entropy of thecombined image abstraction; determining entropy of the known imageabstraction and entropy of the query image abstraction; combining theentropy of the known image abstraction and the entropy of the queryimage abstraction to generate an entropy of underlying images; andgenerating a dissimilarity metric by subtracting the entropy of theunderlying images from the entropy of the combined image abstraction,wherein determining whether the query image is similar to the knownimage comprises using the dissimilarity metric to determine whether thequery image is similar to the known image.
 30. The method of claim 18,wherein the query image abstraction comprises a first query imageabstraction based on a first part of the query image; wherein a secondquery image abstraction is based on a second part of the query image,the first part of the query image being different from the second partof the query image; wherein combining at least a part of the known imageabstraction with at least a part of the query image abstraction togenerate a combined image abstraction comprises: generating a firstcombined image abstraction by combining at least a part of the knownimage abstraction with at least a part of the first query imageabstraction; generating a second combined image abstraction by combiningat least a part of the known image abstraction with at least a part ofthe second query image abstraction; wherein comparing the combined imageabstraction with at least a part of one or both of the known imageabstraction and the query image abstraction comprises: generating afirst score indicative of randomness of the first combined imageabstraction with at least a part of one or both of the known imageabstraction and the first query image abstraction; generating a secondscore indicative of randomness of the second combined image abstractionwith at least a part of one or both of the known image abstraction andthe second query image abstraction; wherein determining whether thequery image is similar to the known image comprises: comparing the firstscore with the second score; and determining whether the first part ofthe query image or the second part of the query image is similar to theknown image based on comparing the first score with the second score.31. An apparatus for analyzing a known image abstraction and a queryimage abstraction to determine whether a query image is similar to aknown image, the apparatus comprising: at least one memory configured tostore the known image abstraction and the query image abstraction, theknown image abstraction being based on part or all of the known image,the query image abstraction being based on part or all of the queryimage, the known image and the query image being of a same type; and acontroller in communication with the memory and configured to: combineat least a part of the known image abstraction with at least a part ofthe query image abstraction to generate a combined image abstraction;and compare the combined image abstraction with at least a part of oneor both of the known image abstraction and the query image abstraction,and determine whether the query image is similar to the known imagebased on comparing the combined image abstraction with at least a partof one or both of the known image abstraction and the query imageabstraction.
 32. The apparatus of claim 31, wherein the controller isconfigured to compare the combined image abstraction with at least apart of one or both of the known image abstraction and the query imageabstraction by determining whether the combined image abstraction isless chaotic than chaos associated with each of the known imageabstraction and the query image abstraction.
 33. The apparatus of claim31, wherein the known image abstraction comprises a known Bag ofFeatures (BoF) description, the known BoF description representing acloud of points in multidimensional feature space; and the query imageabstraction comprises a query BoF description.
 34. The apparatus ofclaim 31, wherein the controller is configured to compare the combinedimage abstraction with at least a part of one or both of the known imageabstraction and the query image abstraction by comparing entropy of thecombined image with entropy of one or both of the known imageabstraction and the query image abstraction.
 35. The apparatus of claim31, wherein the known image abstraction comprises an object abstractionassociated with an object being tracked by a video tracking system;wherein the query image abstraction is associated with an image patch ina video frame; and wherein the controller is further configured toperform foreground/background segmentation to generate a list ofmultiple image patches in the video frame; generate an abstraction foreach of the multiple image patches; iteratively combine the objectabstraction with each of the abstractions for the multiple image patchesto generate combined abstractions; and analyze the combined abstractionsto determine which of the multiple image patches is most similar to theobject abstraction.
 36. The apparatus of claim 35, wherein the objectabstraction is updated based on the image patch most similar to theobject abstraction.